SunoHQ is a no-code platform for deploying multilingual AI voice agents for small businesses. It provides an end-to-end conversational pipeline that handles speech recognition, retrieval, reasoning, and speech synthesis with minimal setup.
SunoHQ enables businesses to deploy AI agents that can:
- Answer customer queries using natural voice conversations
- Handle multilingual interactions common in India (Hindi, English, mixed speech)
- Operate 24/7 across messaging platforms
- Stay grounded in business-specific knowledge
Current deployment supports Telegram voice and text. WhatsApp and telephony integrations are in progress.
SunoHQ runs a conversational loop optimized for low latency:
User Audio -> Speech-to-Text -> Retrieval + LLM -> Text-to-Speech -> Audio Reply
| Layer | Responsibility |
|---|---|
| STT | Transcribe user audio input |
| Retrieval | Fetch business-specific knowledge |
| LLM | Generate contextual responses |
| TTS | Convert response into speech |
The system prioritizes short responses and real-time conversational feel rather than long-form generation.
- Hindi and English support with code-switching
- Regional conversational tuning
- Voice pacing suitable for messaging platforms
Businesses define:
- Agent tone and persona
- FAQs and knowledge base
- Operating hours and metadata
No prompt engineering required.
- Telegram supported today
- WhatsApp integration in progress
- Phone call support planned
The backend is channel-agnostic by design.
Captures qualitative interaction signals such as frustration and positive engagement. This enables insights beyond raw transcripts.
Each business is mapped to an isolated semantic knowledge store backed by a vector database.
- Business uploads FAQs or text data
- Text is embedded into vectors
- Stored with business-level isolation
- Retrieved during inference using similarity search
This ensures responses remain grounded and reduces hallucination in domain-specific queries.
The platform integrates a full speech loop:
- Speech-to-text for multilingual transcription
- Conversational LLM for reasoning
- Text-to-speech for natural voice replies
The system is tuned for:
- Short outputs suitable for voice
- Low perceived latency
- Natural prosody for Indian users
- Python services
- Async-first architecture
- Modular AI service wrappers
- STT and TTS speech models
- Conversational LLM for responses
- Embedding models for retrieval
- Hosted vector database
- API-driven AI orchestration
- Multi-agent workflows
- CRM integrations
- Call analytics and summaries
- Branded voice agents
- Edge-friendly deployments